{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing an initial dataset\n",
    "\n",
    "In this notebook, we will quickly explore a real dataset of questions from writers.stackexchange.com. The dataset was initially sourced from the [archive](https://archive.org/details/stackexchange).\n",
    "\n",
    "First, we will load the data. If you are loading a different csv, make sure you have pre-processed the raw xml using the `ml_editor` python package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from tqdm import tqdm\n",
    "from bs4 import BeautifulSoup\n",
    "import xml.etree.ElementTree as ElT\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "import pandas as pd\n",
    "\n",
    "from pathlib import Path\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "df = pd.read_csv(Path('../data/writers.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data format\n",
    "\n",
    "Let's start by thinking through how we would like to format the data. Amongst other decisions, we will need to decide which label we should give our model.\n",
    "\n",
    "We want a model that measures the quality of a question. To that end, we could use:\n",
    "- The number of upvotes a question gets\n",
    "- The number of answers a question gets, or whether they get an answer at all\n",
    "- Whether an answer was marked as accepted or not\n",
    "\n",
    "First, let's format our dataset to reconcile questions and associated answers, and verify that they match up.\n",
    "\n",
    "We will start by filling missing values, as well as adding two features (`text_len` and `is_question`) we will use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start by changing types to make processing easier\n",
    "df[\"AnswerCount\"] = df[\"AnswerCount\"].fillna(-1)\n",
    "df[\"AnswerCount\"] = df[\"AnswerCount\"].astype(int)\n",
    "df[\"PostTypeId\"] = df[\"PostTypeId\"].astype(int)\n",
    "df[\"Id\"] = df[\"Id\"].astype(int)\n",
    "df.set_index(\"Id\", inplace=True, drop=False)\n",
    "\n",
    "# Add measure of the length of a post\n",
    "df[\"full_text\"] = df[\"Title\"].str.cat(df[\"body_text\"], sep=\" \", na_rep=\"\")\n",
    "df[\"text_len\"] = df[\"full_text\"].str.len()\n",
    "\n",
    "# A question is a post of id 1\n",
    "df[\"is_question\"] = df[\"PostTypeId\"] == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data quality\n",
    "\n",
    "Let's examine the quality of the data in this dataset, starting by answering the questions below\n",
    "\n",
    "- How much of the data is missing?\n",
    "- What is the quality of the text?\n",
    "- Do the answers match the questions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 34330 entries, 1 to 42885\n",
      "Data columns (total 26 columns):\n",
      "Unnamed: 0               34330 non-null int64\n",
      "AcceptedAnswerId         4124 non-null float64\n",
      "AnswerCount              34330 non-null int64\n",
      "Body                     34256 non-null object\n",
      "ClosedDate               969 non-null object\n",
      "CommentCount             34330 non-null int64\n",
      "CommunityOwnedDate       186 non-null object\n",
      "CreationDate             34330 non-null object\n",
      "FavoriteCount            3307 non-null float64\n",
      "Id                       34330 non-null int64\n",
      "LastActivityDate         34330 non-null object\n",
      "LastEditDate             11201 non-null object\n",
      "LastEditorDisplayName    614 non-null object\n",
      "LastEditorUserId         10648 non-null float64\n",
      "OwnerDisplayName         1976 non-null object\n",
      "OwnerUserId              32792 non-null float64\n",
      "ParentId                 25679 non-null float64\n",
      "PostTypeId               34330 non-null int64\n",
      "Score                    34330 non-null int64\n",
      "Tags                     7971 non-null object\n",
      "Title                    7971 non-null object\n",
      "ViewCount                7971 non-null float64\n",
      "body_text                34256 non-null object\n",
      "full_text                34330 non-null object\n",
      "text_len                 34330 non-null int64\n",
      "is_question              34330 non-null bool\n",
      "dtypes: bool(1), float64(6), int64(7), object(12)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Start by displaying columns and counts of non-null entries\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a little over 34.000 posts which consist of both questions and answers. \n",
    "\n",
    "Looking at the `Body` column, it appears that it is null in `34330 - 34256 = 74` rows. Let's take a look at these rows to see if we should remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>AcceptedAnswerId</th>\n",
       "      <th>AnswerCount</th>\n",
       "      <th>Body</th>\n",
       "      <th>ClosedDate</th>\n",
       "      <th>CommentCount</th>\n",
       "      <th>CommunityOwnedDate</th>\n",
       "      <th>CreationDate</th>\n",
       "      <th>FavoriteCount</th>\n",
       "      <th>Id</th>\n",
       "      <th>...</th>\n",
       "      <th>ParentId</th>\n",
       "      <th>PostTypeId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Title</th>\n",
       "      <th>ViewCount</th>\n",
       "      <th>body_text</th>\n",
       "      <th>full_text</th>\n",
       "      <th>text_len</th>\n",
       "      <th>is_question</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2145</th>\n",
       "      <td>1959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-03-22T19:49:56.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2145</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2147</th>\n",
       "      <td>1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-03-22T19:51:05.897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2147</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>2029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-03-24T19:35:10.353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2215</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>2032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-03-24T19:41:38.677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2218</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225</th>\n",
       "      <td>2039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-03-24T19:58:59.833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2225</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>2041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-03-24T20:05:07.753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2227</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>2047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-03-24T20:22:44.603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2233</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2238</th>\n",
       "      <td>2052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-03-24T20:38:42.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2238</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3138</th>\n",
       "      <td>2850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-06-19T20:18:13.253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3138</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5148</th>\n",
       "      <td>4658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-03-03T21:24:29.283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5148</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5150</th>\n",
       "      <td>4660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-03-03T21:32:34.593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5150</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5152</th>\n",
       "      <td>4662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-03-03T21:34:05.107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5152</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5162</th>\n",
       "      <td>4672</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-03-06T05:06:18.340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5162</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5551</th>\n",
       "      <td>5021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-05-01T03:58:38.053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5551</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5553</th>\n",
       "      <td>5023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-05-01T04:00:25.867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5553</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5555</th>\n",
       "      <td>5025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-05-01T04:01:33.213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5555</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5797</th>\n",
       "      <td>5242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-05-31T21:53:43.603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5797</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5832</th>\n",
       "      <td>5277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-06-03T01:46:28.573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5832</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6261</th>\n",
       "      <td>5646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-08-25T22:24:34.940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6261</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6313</th>\n",
       "      <td>5681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-09T05:10:35.357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6313</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6360</th>\n",
       "      <td>5720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-19T05:17:35.503</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6360</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362</th>\n",
       "      <td>5722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-19T05:18:18.670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6362</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6364</th>\n",
       "      <td>5724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-09-19T05:18:54.503</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6364</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8125</th>\n",
       "      <td>7195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-11T15:55:30.707</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8125</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8481</th>\n",
       "      <td>7502</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-07-24T02:22:24.207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8481</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8731</th>\n",
       "      <td>7717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-08-29T10:47:45.753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8731</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9915</th>\n",
       "      <td>8716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-04T23:43:51.240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9915</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>8769</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-09T17:20:20.917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9978</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>8771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-09T17:22:27.567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9980</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>8773</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-01-09T17:23:50.717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9982</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25975</th>\n",
       "      <td>18942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-10T19:34:59.120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25975</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25976</th>\n",
       "      <td>18943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-10T19:34:59.120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25976</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26248</th>\n",
       "      <td>19194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-24T16:52:06.440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26248</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27648</th>\n",
       "      <td>20448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-21T17:03:01.877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27648</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31270</th>\n",
       "      <td>23690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-11-06T16:18:19.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31270</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31512</th>\n",
       "      <td>23911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-11-17T23:54:37.853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31512</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33618</th>\n",
       "      <td>25879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-20T16:14:27.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33618</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33622</th>\n",
       "      <td>25883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-20T16:23:12.540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33622</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33624</th>\n",
       "      <td>25885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-20T16:24:48.927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33624</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33676</th>\n",
       "      <td>25931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-21T16:27:02.373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33676</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34225</th>\n",
       "      <td>26438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-12T18:53:50.403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34225</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34236</th>\n",
       "      <td>26449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-12T21:49:22.623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34236</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34238</th>\n",
       "      <td>26451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-12T21:54:23.803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34238</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34240</th>\n",
       "      <td>26453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-12T21:57:55.510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34240</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34968</th>\n",
       "      <td>27124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-11T05:16:59.860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34968</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36554</th>\n",
       "      <td>28626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-05-30T12:55:01.670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36554</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37125</th>\n",
       "      <td>29145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-06-21T14:31:26.603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37125</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37794</th>\n",
       "      <td>29767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-23T09:02:06.137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37794</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38702</th>\n",
       "      <td>30597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-05T10:45:41.983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38702</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38869</th>\n",
       "      <td>30741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-12T14:14:23.750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38869</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39531</th>\n",
       "      <td>31326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-19T14:40:59.737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39531</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39533</th>\n",
       "      <td>31328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-19T14:45:08.407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39533</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39957</th>\n",
       "      <td>31712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-07T10:37:35.183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39957</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40902</th>\n",
       "      <td>32535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-26T12:40:16.647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40902</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41952</th>\n",
       "      <td>33453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-05T09:24:48.960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41952</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42223</th>\n",
       "      <td>33706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-14T09:42:01.813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42223</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42549</th>\n",
       "      <td>34006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-24T05:18:39.920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42549</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42551</th>\n",
       "      <td>34008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-24T05:26:20.740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42551</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42629</th>\n",
       "      <td>34085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-25T22:45:38.457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42629</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42787</th>\n",
       "      <td>34237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-03-01T01:51:56.043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42787</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  AcceptedAnswerId  AnswerCount Body ClosedDate  \\\n",
       "Id                                                                 \n",
       "2145         1959               NaN           -1  NaN        NaN   \n",
       "2147         1961               NaN           -1  NaN        NaN   \n",
       "2215         2029               NaN           -1  NaN        NaN   \n",
       "2218         2032               NaN           -1  NaN        NaN   \n",
       "2225         2039               NaN           -1  NaN        NaN   \n",
       "2227         2041               NaN           -1  NaN        NaN   \n",
       "2233         2047               NaN           -1  NaN        NaN   \n",
       "2238         2052               NaN           -1  NaN        NaN   \n",
       "3138         2850               NaN           -1  NaN        NaN   \n",
       "5148         4658               NaN           -1  NaN        NaN   \n",
       "5150         4660               NaN           -1  NaN        NaN   \n",
       "5152         4662               NaN           -1  NaN        NaN   \n",
       "5162         4672               NaN           -1  NaN        NaN   \n",
       "5551         5021               NaN           -1  NaN        NaN   \n",
       "5553         5023               NaN           -1  NaN        NaN   \n",
       "5555         5025               NaN           -1  NaN        NaN   \n",
       "5797         5242               NaN           -1  NaN        NaN   \n",
       "5832         5277               NaN           -1  NaN        NaN   \n",
       "6261         5646               NaN           -1  NaN        NaN   \n",
       "6313         5681               NaN           -1  NaN        NaN   \n",
       "6360         5720               NaN           -1  NaN        NaN   \n",
       "6362         5722               NaN           -1  NaN        NaN   \n",
       "6364         5724               NaN           -1  NaN        NaN   \n",
       "8125         7195               NaN           -1  NaN        NaN   \n",
       "8481         7502               NaN           -1  NaN        NaN   \n",
       "8731         7717               NaN           -1  NaN        NaN   \n",
       "9915         8716               NaN           -1  NaN        NaN   \n",
       "9978         8769               NaN           -1  NaN        NaN   \n",
       "9980         8771               NaN           -1  NaN        NaN   \n",
       "9982         8773               NaN           -1  NaN        NaN   \n",
       "...           ...               ...          ...  ...        ...   \n",
       "25975       18942               NaN           -1  NaN        NaN   \n",
       "25976       18943               NaN           -1  NaN        NaN   \n",
       "26248       19194               NaN           -1  NaN        NaN   \n",
       "27648       20448               NaN           -1  NaN        NaN   \n",
       "31270       23690               NaN           -1  NaN        NaN   \n",
       "31512       23911               NaN           -1  NaN        NaN   \n",
       "33618       25879               NaN           -1  NaN        NaN   \n",
       "33622       25883               NaN           -1  NaN        NaN   \n",
       "33624       25885               NaN           -1  NaN        NaN   \n",
       "33676       25931               NaN           -1  NaN        NaN   \n",
       "34225       26438               NaN           -1  NaN        NaN   \n",
       "34236       26449               NaN           -1  NaN        NaN   \n",
       "34238       26451               NaN           -1  NaN        NaN   \n",
       "34240       26453               NaN           -1  NaN        NaN   \n",
       "34968       27124               NaN           -1  NaN        NaN   \n",
       "36554       28626               NaN           -1  NaN        NaN   \n",
       "37125       29145               NaN           -1  NaN        NaN   \n",
       "37794       29767               NaN           -1  NaN        NaN   \n",
       "38702       30597               NaN           -1  NaN        NaN   \n",
       "38869       30741               NaN           -1  NaN        NaN   \n",
       "39531       31326               NaN           -1  NaN        NaN   \n",
       "39533       31328               NaN           -1  NaN        NaN   \n",
       "39957       31712               NaN           -1  NaN        NaN   \n",
       "40902       32535               NaN           -1  NaN        NaN   \n",
       "41952       33453               NaN           -1  NaN        NaN   \n",
       "42223       33706               NaN           -1  NaN        NaN   \n",
       "42549       34006               NaN           -1  NaN        NaN   \n",
       "42551       34008               NaN           -1  NaN        NaN   \n",
       "42629       34085               NaN           -1  NaN        NaN   \n",
       "42787       34237               NaN           -1  NaN        NaN   \n",
       "\n",
       "       CommentCount CommunityOwnedDate             CreationDate  \\\n",
       "Id                                                                \n",
       "2145              0                NaN  2011-03-22T19:49:56.600   \n",
       "2147              0                NaN  2011-03-22T19:51:05.897   \n",
       "2215              0                NaN  2011-03-24T19:35:10.353   \n",
       "2218              0                NaN  2011-03-24T19:41:38.677   \n",
       "2225              0                NaN  2011-03-24T19:58:59.833   \n",
       "2227              0                NaN  2011-03-24T20:05:07.753   \n",
       "2233              0                NaN  2011-03-24T20:22:44.603   \n",
       "2238              0                NaN  2011-03-24T20:38:42.200   \n",
       "3138              0                NaN  2011-06-19T20:18:13.253   \n",
       "5148              0                NaN  2012-03-03T21:24:29.283   \n",
       "5150              0                NaN  2012-03-03T21:32:34.593   \n",
       "5152              0                NaN  2012-03-03T21:34:05.107   \n",
       "5162              0                NaN  2012-03-06T05:06:18.340   \n",
       "5551              0                NaN  2012-05-01T03:58:38.053   \n",
       "5553              0                NaN  2012-05-01T04:00:25.867   \n",
       "5555              0                NaN  2012-05-01T04:01:33.213   \n",
       "5797              0                NaN  2012-05-31T21:53:43.603   \n",
       "5832              0                NaN  2012-06-03T01:46:28.573   \n",
       "6261              0                NaN  2012-08-25T22:24:34.940   \n",
       "6313              0                NaN  2012-09-09T05:10:35.357   \n",
       "6360              0                NaN  2012-09-19T05:17:35.503   \n",
       "6362              0                NaN  2012-09-19T05:18:18.670   \n",
       "6364              0                NaN  2012-09-19T05:18:54.503   \n",
       "8125              0                NaN  2013-06-11T15:55:30.707   \n",
       "8481              0                NaN  2013-07-24T02:22:24.207   \n",
       "8731              0                NaN  2013-08-29T10:47:45.753   \n",
       "9915              0                NaN  2014-01-04T23:43:51.240   \n",
       "9978              0                NaN  2014-01-09T17:20:20.917   \n",
       "9980              0                NaN  2014-01-09T17:22:27.567   \n",
       "9982              0                NaN  2014-01-09T17:23:50.717   \n",
       "...             ...                ...                      ...   \n",
       "25975             0                NaN  2017-01-10T19:34:59.120   \n",
       "25976             0                NaN  2017-01-10T19:34:59.120   \n",
       "26248             0                NaN  2017-01-24T16:52:06.440   \n",
       "27648             0                NaN  2017-04-21T17:03:01.877   \n",
       "31270             0                NaN  2017-11-06T16:18:19.400   \n",
       "31512             0                NaN  2017-11-17T23:54:37.853   \n",
       "33618             0                NaN  2018-02-20T16:14:27.410   \n",
       "33622             0                NaN  2018-02-20T16:23:12.540   \n",
       "33624             0                NaN  2018-02-20T16:24:48.927   \n",
       "33676             0                NaN  2018-02-21T16:27:02.373   \n",
       "34225             0                NaN  2018-03-12T18:53:50.403   \n",
       "34236             0                NaN  2018-03-12T21:49:22.623   \n",
       "34238             0                NaN  2018-03-12T21:54:23.803   \n",
       "34240             0                NaN  2018-03-12T21:57:55.510   \n",
       "34968             0                NaN  2018-04-11T05:16:59.860   \n",
       "36554             0                NaN  2018-05-30T12:55:01.670   \n",
       "37125             0                NaN  2018-06-21T14:31:26.603   \n",
       "37794             0                NaN  2018-07-23T09:02:06.137   \n",
       "38702             0                NaN  2018-09-05T10:45:41.983   \n",
       "38869             0                NaN  2018-09-12T14:14:23.750   \n",
       "39531             0                NaN  2018-10-19T14:40:59.737   \n",
       "39533             0                NaN  2018-10-19T14:45:08.407   \n",
       "39957             0                NaN  2018-11-07T10:37:35.183   \n",
       "40902             0                NaN  2018-12-26T12:40:16.647   \n",
       "41952             0                NaN  2019-02-05T09:24:48.960   \n",
       "42223             0                NaN  2019-02-14T09:42:01.813   \n",
       "42549             0                NaN  2019-02-24T05:18:39.920   \n",
       "42551             0                NaN  2019-02-24T05:26:20.740   \n",
       "42629             0                NaN  2019-02-25T22:45:38.457   \n",
       "42787             0                NaN  2019-03-01T01:51:56.043   \n",
       "\n",
       "       FavoriteCount     Id  ... ParentId PostTypeId Score  Tags Title  \\\n",
       "Id                           ...                                         \n",
       "2145             NaN   2145  ...      NaN          5     0   NaN   NaN   \n",
       "2147             NaN   2147  ...      NaN          5     0   NaN   NaN   \n",
       "2215             NaN   2215  ...      NaN          5     0   NaN   NaN   \n",
       "2218             NaN   2218  ...      NaN          5     0   NaN   NaN   \n",
       "2225             NaN   2225  ...      NaN          5     0   NaN   NaN   \n",
       "2227             NaN   2227  ...      NaN          5     0   NaN   NaN   \n",
       "2233             NaN   2233  ...      NaN          5     0   NaN   NaN   \n",
       "2238             NaN   2238  ...      NaN          5     0   NaN   NaN   \n",
       "3138             NaN   3138  ...      NaN          5     0   NaN   NaN   \n",
       "5148             NaN   5148  ...      NaN          5     0   NaN   NaN   \n",
       "5150             NaN   5150  ...      NaN          5     0   NaN   NaN   \n",
       "5152             NaN   5152  ...      NaN          5     0   NaN   NaN   \n",
       "5162             NaN   5162  ...      NaN          5     0   NaN   NaN   \n",
       "5551             NaN   5551  ...      NaN          5     0   NaN   NaN   \n",
       "5553             NaN   5553  ...      NaN          5     0   NaN   NaN   \n",
       "5555             NaN   5555  ...      NaN          5     0   NaN   NaN   \n",
       "5797             NaN   5797  ...      NaN          5     0   NaN   NaN   \n",
       "5832             NaN   5832  ...      NaN          5     0   NaN   NaN   \n",
       "6261             NaN   6261  ...      NaN          5     0   NaN   NaN   \n",
       "6313             NaN   6313  ...      NaN          5     0   NaN   NaN   \n",
       "6360             NaN   6360  ...      NaN          5     0   NaN   NaN   \n",
       "6362             NaN   6362  ...      NaN          5     0   NaN   NaN   \n",
       "6364             NaN   6364  ...      NaN          5     0   NaN   NaN   \n",
       "8125             NaN   8125  ...      NaN          5     0   NaN   NaN   \n",
       "8481             NaN   8481  ...      NaN          5     0   NaN   NaN   \n",
       "8731             NaN   8731  ...      NaN          5     0   NaN   NaN   \n",
       "9915             NaN   9915  ...      NaN          5     0   NaN   NaN   \n",
       "9978             NaN   9978  ...      NaN          5     0   NaN   NaN   \n",
       "9980             NaN   9980  ...      NaN          5     0   NaN   NaN   \n",
       "9982             NaN   9982  ...      NaN          5     0   NaN   NaN   \n",
       "...              ...    ...  ...      ...        ...   ...   ...   ...   \n",
       "25975            NaN  25975  ...      NaN          5     0   NaN   NaN   \n",
       "25976            NaN  25976  ...      NaN          4     0   NaN   NaN   \n",
       "26248            NaN  26248  ...      NaN          5     0   NaN   NaN   \n",
       "27648            NaN  27648  ...      NaN          5     0   NaN   NaN   \n",
       "31270            NaN  31270  ...      NaN          5     0   NaN   NaN   \n",
       "31512            NaN  31512  ...      NaN          5     0   NaN   NaN   \n",
       "33618            NaN  33618  ...      NaN          5     0   NaN   NaN   \n",
       "33622            NaN  33622  ...      NaN          5     0   NaN   NaN   \n",
       "33624            NaN  33624  ...      NaN          5     0   NaN   NaN   \n",
       "33676            NaN  33676  ...      NaN          5     0   NaN   NaN   \n",
       "34225            NaN  34225  ...      NaN          5     0   NaN   NaN   \n",
       "34236            NaN  34236  ...      NaN          5     0   NaN   NaN   \n",
       "34238            NaN  34238  ...      NaN          5     0   NaN   NaN   \n",
       "34240            NaN  34240  ...      NaN          5     0   NaN   NaN   \n",
       "34968            NaN  34968  ...      NaN          5     0   NaN   NaN   \n",
       "36554            NaN  36554  ...      NaN          5     0   NaN   NaN   \n",
       "37125            NaN  37125  ...      NaN          5     0   NaN   NaN   \n",
       "37794            NaN  37794  ...      NaN          5     0   NaN   NaN   \n",
       "38702            NaN  38702  ...      NaN          5     0   NaN   NaN   \n",
       "38869            NaN  38869  ...      NaN          5     0   NaN   NaN   \n",
       "39531            NaN  39531  ...      NaN          5     0   NaN   NaN   \n",
       "39533            NaN  39533  ...      NaN          5     0   NaN   NaN   \n",
       "39957            NaN  39957  ...      NaN          5     0   NaN   NaN   \n",
       "40902            NaN  40902  ...      NaN          5     0   NaN   NaN   \n",
       "41952            NaN  41952  ...      NaN          5     0   NaN   NaN   \n",
       "42223            NaN  42223  ...      NaN          5     0   NaN   NaN   \n",
       "42549            NaN  42549  ...      NaN          5     0   NaN   NaN   \n",
       "42551            NaN  42551  ...      NaN          5     0   NaN   NaN   \n",
       "42629            NaN  42629  ...      NaN          5     0   NaN   NaN   \n",
       "42787            NaN  42787  ...      NaN          5     0   NaN   NaN   \n",
       "\n",
       "       ViewCount  body_text  full_text  text_len is_question  \n",
       "Id                                                            \n",
       "2145         NaN        NaN                    1       False  \n",
       "2147         NaN        NaN                    1       False  \n",
       "2215         NaN        NaN                    1       False  \n",
       "2218         NaN        NaN                    1       False  \n",
       "2225         NaN        NaN                    1       False  \n",
       "2227         NaN        NaN                    1       False  \n",
       "2233         NaN        NaN                    1       False  \n",
       "2238         NaN        NaN                    1       False  \n",
       "3138         NaN        NaN                    1       False  \n",
       "5148         NaN        NaN                    1       False  \n",
       "5150         NaN        NaN                    1       False  \n",
       "5152         NaN        NaN                    1       False  \n",
       "5162         NaN        NaN                    1       False  \n",
       "5551         NaN        NaN                    1       False  \n",
       "5553         NaN        NaN                    1       False  \n",
       "5555         NaN        NaN                    1       False  \n",
       "5797         NaN        NaN                    1       False  \n",
       "5832         NaN        NaN                    1       False  \n",
       "6261         NaN        NaN                    1       False  \n",
       "6313         NaN        NaN                    1       False  \n",
       "6360         NaN        NaN                    1       False  \n",
       "6362         NaN        NaN                    1       False  \n",
       "6364         NaN        NaN                    1       False  \n",
       "8125         NaN        NaN                    1       False  \n",
       "8481         NaN        NaN                    1       False  \n",
       "8731         NaN        NaN                    1       False  \n",
       "9915         NaN        NaN                    1       False  \n",
       "9978         NaN        NaN                    1       False  \n",
       "9980         NaN        NaN                    1       False  \n",
       "9982         NaN        NaN                    1       False  \n",
       "...          ...        ...        ...       ...         ...  \n",
       "25975        NaN        NaN                    1       False  \n",
       "25976        NaN        NaN                    1       False  \n",
       "26248        NaN        NaN                    1       False  \n",
       "27648        NaN        NaN                    1       False  \n",
       "31270        NaN        NaN                    1       False  \n",
       "31512        NaN        NaN                    1       False  \n",
       "33618        NaN        NaN                    1       False  \n",
       "33622        NaN        NaN                    1       False  \n",
       "33624        NaN        NaN                    1       False  \n",
       "33676        NaN        NaN                    1       False  \n",
       "34225        NaN        NaN                    1       False  \n",
       "34236        NaN        NaN                    1       False  \n",
       "34238        NaN        NaN                    1       False  \n",
       "34240        NaN        NaN                    1       False  \n",
       "34968        NaN        NaN                    1       False  \n",
       "36554        NaN        NaN                    1       False  \n",
       "37125        NaN        NaN                    1       False  \n",
       "37794        NaN        NaN                    1       False  \n",
       "38702        NaN        NaN                    1       False  \n",
       "38869        NaN        NaN                    1       False  \n",
       "39531        NaN        NaN                    1       False  \n",
       "39533        NaN        NaN                    1       False  \n",
       "39957        NaN        NaN                    1       False  \n",
       "40902        NaN        NaN                    1       False  \n",
       "41952        NaN        NaN                    1       False  \n",
       "42223        NaN        NaN                    1       False  \n",
       "42549        NaN        NaN                    1       False  \n",
       "42551        NaN        NaN                    1       False  \n",
       "42629        NaN        NaN                    1       False  \n",
       "42787        NaN        NaN                    1       False  \n",
       "\n",
       "[74 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Body\"].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the null bodys are of  PostTypeId 4 or 5. \n",
    "\n",
    "The readme file that accompanied the archive only mentions PostTypeIds of 1 (questions) and 2 (answers). We will remove all rows not marked `PostTypeId` 1 or 2, since we are only interested in questions and answers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 33650 entries, 1 to 42885\n",
      "Data columns (total 26 columns):\n",
      "Unnamed: 0               33650 non-null int64\n",
      "AcceptedAnswerId         4124 non-null float64\n",
      "AnswerCount              33650 non-null int64\n",
      "Body                     33650 non-null object\n",
      "ClosedDate               969 non-null object\n",
      "CommentCount             33650 non-null int64\n",
      "CommunityOwnedDate       186 non-null object\n",
      "CreationDate             33650 non-null object\n",
      "FavoriteCount            3307 non-null float64\n",
      "Id                       33650 non-null int64\n",
      "LastActivityDate         33650 non-null object\n",
      "LastEditDate             10521 non-null object\n",
      "LastEditorDisplayName    606 non-null object\n",
      "LastEditorUserId         9975 non-null float64\n",
      "OwnerDisplayName         1971 non-null object\n",
      "OwnerUserId              32117 non-null float64\n",
      "ParentId                 25679 non-null float64\n",
      "PostTypeId               33650 non-null int64\n",
      "Score                    33650 non-null int64\n",
      "Tags                     7971 non-null object\n",
      "Title                    7971 non-null object\n",
      "ViewCount                7971 non-null float64\n",
      "body_text                33650 non-null object\n",
      "full_text                33650 non-null object\n",
      "text_len                 33650 non-null int64\n",
      "is_question              33650 non-null bool\n",
      "dtypes: bool(1), float64(6), int64(7), object(12)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df = df[df[\"PostTypeId\"].isin([1,2])]\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at a few questions and answers and verify that they match, and that the text is readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body_text</th>\n",
       "      <th>body_text_answer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n</td>\n",
       "      <td>When I'm thinking about where I learned most how to write, I think that reading was the most important guide to me. This may sound silly, but by reading good written newspaper articles (facts, opinions, scientific articles and most of all, criticisms of films and music), I learned how others did the job, what works and what doesn't. In my own writing, I try to mimic other people's styles that I liked. Moreover, I learn new things by reading, giving me a broader background that I need when re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>What kind of story is better suited for each point of view? Are there advantages or disadvantages inherent to them?\\nFor example, writing in the first person you are always following a character, while in the third person you can \"jump\" between story lines.\\n</td>\n",
       "      <td>With a story in first person, you are intending the reader to become much more attached to the main character.  Since the reader sees what that character sees and feels what that character feels, the reader will have an emotional investment in that character.  Third person does not have this close tie; a reader can become emotionally invested but it will not be as strong as it will be in first person.\\nContrarily, you cannot have multiple point characters when you use first person without ex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I finished my novel, and everyone I've talked to says I need an agent. How do I find one?\\n</td>\n",
       "      <td>Try and find a list of agents who write in your genre. Check out their websites!\\nFind out if they are accepting new clients. If they aren't, then check out another agent. But if they are, try sending them a few chapters from your story, a brief, and a short cover letter asking them to represent you.\\nIn the cover letter mention your previous publication credits. If sent via post, then I suggest you give them a means of reply, whether it be an email or a stamped, addressed envelope.\\nAgents ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                            body_text  \\\n",
       "Id                                                                                                                                                                                                                                                                                                                      \n",
       "1   I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n   \n",
       "2                                                 What kind of story is better suited for each point of view? Are there advantages or disadvantages inherent to them?\\nFor example, writing in the first person you are always following a character, while in the third person you can \"jump\" between story lines.\\n   \n",
       "3                                                                                                                                                                                                                         I finished my novel, and everyone I've talked to says I need an agent. How do I find one?\\n   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       body_text_answer  \n",
       "Id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       \n",
       "1   When I'm thinking about where I learned most how to write, I think that reading was the most important guide to me. This may sound silly, but by reading good written newspaper articles (facts, opinions, scientific articles and most of all, criticisms of films and music), I learned how others did the job, what works and what doesn't. In my own writing, I try to mimic other people's styles that I liked. Moreover, I learn new things by reading, giving me a broader background that I need when re...  \n",
       "2   With a story in first person, you are intending the reader to become much more attached to the main character.  Since the reader sees what that character sees and feels what that character feels, the reader will have an emotional investment in that character.  Third person does not have this close tie; a reader can become emotionally invested but it will not be as strong as it will be in first person.\\nContrarily, you cannot have multiple point characters when you use first person without ex...  \n",
       "3   Try and find a list of agents who write in your genre. Check out their websites!\\nFind out if they are accepting new clients. If they aren't, then check out another agent. But if they are, try sending them a few chapters from your story, a brief, and a short cover letter asking them to represent you.\\nIn the cover letter mention your previous publication credits. If sent via post, then I suggest you give them a means of reply, whether it be an email or a stamped, addressed envelope.\\nAgents ...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_with_accepted_answers = df[df[\"is_question\"] & ~(df[\"AcceptedAnswerId\"].isna())]\n",
    "q_and_a = questions_with_accepted_answers.join(df[[\"body_text\"]], on=\"AcceptedAnswerId\", how=\"left\", rsuffix=\"_answer\")\n",
    "\n",
    "# Setting this option allows us to display all the data\n",
    "pd.options.display.max_colwidth = 500\n",
    "q_and_a[[\"body_text\", \"body_text_answer\"]][:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far so good, the **questions and answers seem to match**, and the text seems coherent and human readable  except for unescaped newlines characters (\"\\n\").\n",
    "\n",
    "Let's add to each row representing an answer information about the question it was answering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(df[[\"Id\", \"Title\", \"body_text\", \"text_len\", \"Score\", \"AcceptedAnswerId\"]], on=\"ParentId\", how=\"left\", rsuffix='_question')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This allows us to see all answers for a given question like so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>AcceptedAnswerId</th>\n",
       "      <th>AnswerCount</th>\n",
       "      <th>Body</th>\n",
       "      <th>ClosedDate</th>\n",
       "      <th>CommentCount</th>\n",
       "      <th>CommunityOwnedDate</th>\n",
       "      <th>CreationDate</th>\n",
       "      <th>FavoriteCount</th>\n",
       "      <th>Id</th>\n",
       "      <th>...</th>\n",
       "      <th>body_text</th>\n",
       "      <th>full_text</th>\n",
       "      <th>text_len</th>\n",
       "      <th>is_question</th>\n",
       "      <th>Id_question</th>\n",
       "      <th>Title_question</th>\n",
       "      <th>body_text_question</th>\n",
       "      <th>text_len_question</th>\n",
       "      <th>Score_question</th>\n",
       "      <th>AcceptedAnswerId_question</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>&lt;p&gt;BeginningWriters.com has some good articles for beginning writers.&lt;/p&gt;\\n\\n&lt;p&gt;&lt;a href=\"http://beginningwriters.com/\"&gt;http://beginningwriters.com/&lt;/a&gt;&lt;/p&gt;\\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-11-18T20:45:45.533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>BeginningWriters.com has some good articles for beginning writers.\\nhttp://beginningwriters.com/\\n</td>\n",
       "      <td>BeginningWriters.com has some good articles for beginning writers.\\nhttp://beginningwriters.com/\\n</td>\n",
       "      <td>97</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What are some online guides for starting writers?</td>\n",
       "      <td>I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n</td>\n",
       "      <td>352.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>&lt;p&gt;When I'm thinking about where I learned most how to write, I think that &lt;em&gt;reading&lt;/em&gt; was the most important guide to me. This may sound silly, but by reading good written newspaper articles (facts, opinions, scientific articles and most of all, criticisms of films and music), I learned how others did the job, what works and what doesn't. In my own writing, I try to mimic other people's styles that I liked. Moreover, I learn new things by reading, giving me a broader background that I ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-11-18T20:50:16.683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>When I'm thinking about where I learned most how to write, I think that reading was the most important guide to me. This may sound silly, but by reading good written newspaper articles (facts, opinions, scientific articles and most of all, criticisms of films and music), I learned how others did the job, what works and what doesn't. In my own writing, I try to mimic other people's styles that I liked. Moreover, I learn new things by reading, giving me a broader background that I need when re...</td>\n",
       "      <td>When I'm thinking about where I learned most how to write, I think that reading was the most important guide to me. This may sound silly, but by reading good written newspaper articles (facts, opinions, scientific articles and most of all, criticisms of films and music), I learned how others did the job, what works and what doesn't. In my own writing, I try to mimic other people's styles that I liked. Moreover, I learn new things by reading, giving me a broader background that I need when r...</td>\n",
       "      <td>962</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What are some online guides for starting writers?</td>\n",
       "      <td>I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n</td>\n",
       "      <td>352.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>&lt;p&gt;I particularly like &lt;a href=\"http://www.advancedfictionwriting.com/art/snowflake.php\" rel=\"nofollow\" rel=\"nofollow\"&gt;The Snowflake Method&lt;/a&gt; and most of what Randy writes, including his book.&lt;/p&gt;\\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-11-18T23:19:33.157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>I particularly like The Snowflake Method and most of what Randy writes, including his book.\\n</td>\n",
       "      <td>I particularly like The Snowflake Method and most of what Randy writes, including his book.\\n</td>\n",
       "      <td>93</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What are some online guides for starting writers?</td>\n",
       "      <td>I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n</td>\n",
       "      <td>352.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>&lt;p&gt;Reading a lot and loving to read are most important of course, but have you tried writing-related podcasts?&lt;/p&gt;\\n\\n&lt;p&gt;&lt;strong&gt;&lt;a href=\"http://www.writingexcuses.com/\" rel=\"nofollow\"&gt;Writing Excuses&lt;/a&gt;&lt;/strong&gt; got me into writing fiction again. It's a short podcast about how to write fiction well and how to \"break in\" to the industry, from 3 guys who do and did. (I wrote a bit more about it on &lt;a href=\"http://mgowen.com/2010/11/12/writing-excuses/\" rel=\"nofollow\"&gt;my blog&lt;/a&gt;)&lt;/p&gt;\\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-11-19T04:25:03.407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123</td>\n",
       "      <td>...</td>\n",
       "      <td>Reading a lot and loving to read are most important of course, but have you tried writing-related podcasts?\\nWriting Excuses got me into writing fiction again. It's a short podcast about how to write fiction well and how to \"break in\" to the industry, from 3 guys who do and did. (I wrote a bit more about it on my blog)\\n</td>\n",
       "      <td>Reading a lot and loving to read are most important of course, but have you tried writing-related podcasts?\\nWriting Excuses got me into writing fiction again. It's a short podcast about how to write fiction well and how to \"break in\" to the industry, from 3 guys who do and did. (I wrote a bit more about it on my blog)\\n</td>\n",
       "      <td>321</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What are some online guides for starting writers?</td>\n",
       "      <td>I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n</td>\n",
       "      <td>352.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>&lt;p&gt;&lt;a href=\"http://www.writersdigest.com/GeneralMenu/\" rel=\"nofollow\"&gt;Writer's Digest&lt;/a&gt; has many good articles.&lt;/p&gt;\\n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-11-19T20:09:56.347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>254</td>\n",
       "      <td>...</td>\n",
       "      <td>Writer's Digest has many good articles.\\n</td>\n",
       "      <td>Writer's Digest has many good articles.\\n</td>\n",
       "      <td>41</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>What are some online guides for starting writers?</td>\n",
       "      <td>I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n</td>\n",
       "      <td>352.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  AcceptedAnswerId  AnswerCount  \\\n",
       "Id                                               \n",
       "8             5               NaN           -1   \n",
       "15           10               NaN           -1   \n",
       "79           65               NaN           -1   \n",
       "123         106               NaN           -1   \n",
       "254         231               NaN           -1   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Body  \\\n",
       "Id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         \n",
       "8                                                                                                                                                                                                                                                                                                                                                          <p>BeginningWriters.com has some good articles for beginning writers.</p>\\n\\n<p><a href=\"http://beginningwriters.com/\">http://beginningwriters.com/</a></p>\\n   \n",
       "15   <p>When I'm thinking about where I learned most how to write, I think that <em>reading</em> was the most important guide to me. This may sound silly, but by reading good written newspaper articles (facts, opinions, scientific articles and most of all, criticisms of films and music), I learned how others did the job, what works and what doesn't. In my own writing, I try to mimic other people's styles that I liked. Moreover, I learn new things by reading, giving me a broader background that I ...   \n",
       "79                                                                                                                                                                                                                                                                                                              <p>I particularly like <a href=\"http://www.advancedfictionwriting.com/art/snowflake.php\" rel=\"nofollow\" rel=\"nofollow\">The Snowflake Method</a> and most of what Randy writes, including his book.</p>\\n   \n",
       "123           <p>Reading a lot and loving to read are most important of course, but have you tried writing-related podcasts?</p>\\n\\n<p><strong><a href=\"http://www.writingexcuses.com/\" rel=\"nofollow\">Writing Excuses</a></strong> got me into writing fiction again. It's a short podcast about how to write fiction well and how to \"break in\" to the industry, from 3 guys who do and did. (I wrote a bit more about it on <a href=\"http://mgowen.com/2010/11/12/writing-excuses/\" rel=\"nofollow\">my blog</a>)</p>\\n   \n",
       "254                                                                                                                                                                                                                                                                                                                                                                                              <p><a href=\"http://www.writersdigest.com/GeneralMenu/\" rel=\"nofollow\">Writer's Digest</a> has many good articles.</p>\\n   \n",
       "\n",
       "    ClosedDate  CommentCount CommunityOwnedDate             CreationDate  \\\n",
       "Id                                                                         \n",
       "8          NaN             0                NaN  2010-11-18T20:45:45.533   \n",
       "15         NaN             3                NaN  2010-11-18T20:50:16.683   \n",
       "79         NaN             0                NaN  2010-11-18T23:19:33.157   \n",
       "123        NaN             0                NaN  2010-11-19T04:25:03.407   \n",
       "254        NaN             0                NaN  2010-11-19T20:09:56.347   \n",
       "\n",
       "     FavoriteCount   Id  ...  \\\n",
       "Id                       ...   \n",
       "8              NaN    8  ...   \n",
       "15             NaN   15  ...   \n",
       "79             NaN   79  ...   \n",
       "123            NaN  123  ...   \n",
       "254            NaN  254  ...   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               body_text  \\\n",
       "Id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         \n",
       "8                                                                                                                                                                                                                                                                                                                                                                                                                     BeginningWriters.com has some good articles for beginning writers.\\nhttp://beginningwriters.com/\\n   \n",
       "15   When I'm thinking about where I learned most how to write, I think that reading was the most important guide to me. This may sound silly, but by reading good written newspaper articles (facts, opinions, scientific articles and most of all, criticisms of films and music), I learned how others did the job, what works and what doesn't. In my own writing, I try to mimic other people's styles that I liked. Moreover, I learn new things by reading, giving me a broader background that I need when re...   \n",
       "79                                                                                                                                                                                                                                                                                                                                                                                                                         I particularly like The Snowflake Method and most of what Randy writes, including his book.\\n   \n",
       "123                                                                                                                                                                                   Reading a lot and loving to read are most important of course, but have you tried writing-related podcasts?\\nWriting Excuses got me into writing fiction again. It's a short podcast about how to write fiction well and how to \"break in\" to the industry, from 3 guys who do and did. (I wrote a bit more about it on my blog)\\n   \n",
       "254                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Writer's Digest has many good articles.\\n   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               full_text  \\\n",
       "Id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         \n",
       "8                                                                                                                                                                                                                                                                                                                                                                                                                     BeginningWriters.com has some good articles for beginning writers.\\nhttp://beginningwriters.com/\\n   \n",
       "15    When I'm thinking about where I learned most how to write, I think that reading was the most important guide to me. This may sound silly, but by reading good written newspaper articles (facts, opinions, scientific articles and most of all, criticisms of films and music), I learned how others did the job, what works and what doesn't. In my own writing, I try to mimic other people's styles that I liked. Moreover, I learn new things by reading, giving me a broader background that I need when r...   \n",
       "79                                                                                                                                                                                                                                                                                                                                                                                                                         I particularly like The Snowflake Method and most of what Randy writes, including his book.\\n   \n",
       "123                                                                                                                                                                                   Reading a lot and loving to read are most important of course, but have you tried writing-related podcasts?\\nWriting Excuses got me into writing fiction again. It's a short podcast about how to write fiction well and how to \"break in\" to the industry, from 3 guys who do and did. (I wrote a bit more about it on my blog)\\n   \n",
       "254                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Writer's Digest has many good articles.\\n   \n",
       "\n",
       "    text_len  is_question Id_question  \\\n",
       "Id                                      \n",
       "8         97        False         1.0   \n",
       "15       962        False         1.0   \n",
       "79        93        False         1.0   \n",
       "123      321        False         1.0   \n",
       "254       41        False         1.0   \n",
       "\n",
       "                                        Title_question  \\\n",
       "Id                                                       \n",
       "8    What are some online guides for starting writers?   \n",
       "15   What are some online guides for starting writers?   \n",
       "79   What are some online guides for starting writers?   \n",
       "123  What are some online guides for starting writers?   \n",
       "254  What are some online guides for starting writers?   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                    body_text_question  \\\n",
       "Id                                                                                                                                                                                                                                                                                                                       \n",
       "8    I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n   \n",
       "15   I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n   \n",
       "79   I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n   \n",
       "123  I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n   \n",
       "254  I've always wanted to start writing (in a totally amateur way), but whenever I want to start something I instantly get blocked having a lot of questions and doubts.\\nAre there some resources on how to start becoming a writer?\\nI'm thinking something with tips and easy exercises to get the ball rolling.\\n   \n",
       "\n",
       "     text_len_question  Score_question AcceptedAnswerId_question  \n",
       "Id                                                                \n",
       "8                352.0            32.0                      15.0  \n",
       "15               352.0            32.0                      15.0  \n",
       "79               352.0            32.0                      15.0  \n",
       "123              352.0            32.0                      15.0  \n",
       "254              352.0            32.0                      15.0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Id_question\"] ==1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data quantity and distribution\n",
    "\n",
    "Now that we have linked questions and answers, and verified data quality, let's produce some summary statistics.\n",
    "\n",
    "- How many questions are in the dataset?\n",
    "- How many questions received answers?\n",
    "- How long do questions tend to be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7971 total questions \n",
      " 7827  received at least one answer \n",
      " 4124 received an accepted answer\n"
     ]
    }
   ],
   "source": [
    "has_accepted_answer = df[df[\"is_question\"] & ~(df[\"AcceptedAnswerId\"].isna())]\n",
    "received_answers = df[df[\"is_question\"] & (df[\"AnswerCount\"]!=0)]\n",
    "no_answers = df[df[\"is_question\"] & (df[\"AcceptedAnswerId\"].isna()) & (df[\"AnswerCount\"]==0)]\n",
    "\n",
    "print(\"%s total questions \\n %s  received at least one answer \\n %s received an accepted answer\" % (\n",
    "    len(df[df[\"is_question\"]]),\n",
    "    len(received_answers),\n",
    "    len(has_accepted_answer)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most questions have an answer, and only half of those answers have been accepted. Whether a question received an accepted answer could be a good label.\n",
    "\n",
    "Another potential label would be the number of upvotes a question received, denoted in the `Score` column. This label is helpful because we can either treat it as a binary (whether the score is higher or lower than the median) or continuous depending on how granular we would like our model to be.I've plotted the score distribution below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7wAAAKUCAYAAAAnyzTYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xu0pVdZJ+rfC8VNChMQreYSqCjxgkRoKC5eTlsBDYFwO3SLdEdIYtrYSiMeQzcFgkEubTiKtnQfbHMEDaCUNLYSCagxUCptc4sg4SJNhEIJEMSEkOImgbf/WF/BTlG79qrsvWrXmvU8Y6yx1ze/27vWrDEyfpnzm6u6OwAAADCam212AQAAALAIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBF4B1q6r/VlXP2qBr3a2q9lXVzaftPVX1bzfi2tP1Xl9VZ27U9Q7jvs+rqk9W1ceP9L3nVVXPqKrf2Ow6AGCjlN/hBeBQqmpvkm1JbkjypSTvTfKyJBd295dvwrX+bXf/6WGcsyfJK7r7sINYVT07yT26+0cO99yNVFV3S/L+JHfv7k9sZi37VdXOzL7Xu252LQCwKEZ4AZjHI7v7dknunuSCJE9L8pKNvklVbdnoax4l7pbkH4+WsDuagf/dALBOAi8Ac+vu67r74iQ/nOTMqrpXklTVb1XV86b3d6yq11bVp6rqmqr6i6q6WVW9PLPg94fTlOX/WFXbq6qr6pyq+rskb1jRtjLEfEtVvbWqPl1Vr6mqO0z32llVH1lZY1XtraofqKrTkjwjyQ9P9/vraf9XpkhPdT2zqj5cVZ+oqpdV1XHTvv11nFlVfzdNR/7Z1b6bqjpuOv8fpus9c7r+DyS5NMmdpzp+a5Xz/0NVfayqPlpVPzrd+x4H1jxtn1VVb1qx/e1Vden0fb+/qh63Yt/Dq+q9VXV9VV1VVU+tqtsmef2KmvZV1Z2r6tlV9YoV5z6qqt4z9eWeqvqOA77np1bVu6rquqr63aq69Sqf7R5V9WfTcZ+sqt9dse87V9R+dVU9Y2q/VVX95+n7+Oj0/lYr+72qnjZNEf/Nqf0RVfXOqd6/rKrvWnGfp02f//rpO3rIan0JwDgEXgAOW3e/NclHkvxfB9l93rTvGzObCv2M2Sn9hCR/l9lo8dbu/n9XnPP9Sb4jyUNXueUTk/xokjtlNrX6RXPU+EdJ/lOS353ud++DHHbW9DolyTcn2Zrkvx5wzPcl+bYkD0nycytD3wH+S5Ljput8/1Tz2dP07Ycl+ehUx1kHnjiF86cm+cEkJyX5gbU+34pzb5tZoP6dJN+U5PFJXlxV95wOeUmSH59G6O+V5A3d/ZkDatra3R894LrfmuSVSX46s758XWb/s+KWKw57XJLTkpyY5Lsy+y4P5rlJ/iTJ7ZPcNbPvKlV1uyR/muSPktw5yT2SXDad87NJHpTkPknuneQBSZ654pr/LMkdMpt1cG5V/fMkL03y40m+IcmvJ7l4Cs7fluTfJ7n/9D08NMneVWoFYCACLwA31UczCxwH+mJmwfTu3f3F7v6LXnvBiGd392e6+3Or7H95d797CmrPSvK4mha1Wqczkvxyd3+wu/cleXqSxx8wuvzz3f257v7rJH+dWfi6kamWxyd5endf3917k7wwyRPmrONxSX5zxWd89mF8hkck2dvdv9ndN3T3O5L8XpIfmvZ/Mck9q+rru/va7v6rOa/7w0ku6e5Lu/uLSX4pyW2SfM+KY17U3R/t7muS/GFm4fRgvphZML1zd3++u/ePTj8iyce7+4VT+/Xd/ZZp3xlJntPdn+juf0jy87nx9/nlJOd39xemfzfnJvn17n5Ld3+puy9K8oXMQvOXktxq+h5u0d17u/tv5/weAFhiAi8AN9VdklxzkPZfTHJlkj+pqg9W1a45rvX3h7H/w0lukeSOc1V5aHeerrfy2lsyG5neb+Wqyp/NbBT4QHecajrwWnc5jDoO/IzzunuSB07TeD9VVZ/KLCz+s2n/v0zy8CQfnqYVf/dh1PSVOqYFyv4+N/5M83w3SfIfk1SSt05TpH90aj8hyWrB82B9c+cV2//Q3Z9fsX33JOcd8D2ckFnIvjKzkepnJ/lEVe2uqpXXAmBQAi8Ah62q7p9Z8HnTgfumUbrzuvubkzwqyc+seF5ytZHetUaAT1jx/m6ZjRh+Mslnknzdirpuntn023mv+9HMgtLKa9+Q5Oo1zjvQJ/PVUcyV17pqzvM/lq/9jCvd6HPmq2E2mYXQP+vu41e8tnb3TyRJd7+tux+d2XTnP0jyqum8w/puqqqmGuf9TF/R3R/v7h/r7jtnNuX4xdPzyX+f2RTwNe+f2Xeyctr1gfX/fZLnH/A9fF13v3Kq4Xe6+/uma3aSFxzu5wBg+Qi8AMytqr6+qh6RZHdmP2lzxUGOecS0SFEluS6z6aT7f77o6qwecA7lR6rqnlX1dUmek+TV3f2lJP87ya2r6vSqukVmz3jeasV5VyfZXlWr/ffulUn+n6o6saq25qvP/N5wOMVNtbwqyfOr6nZVdfckP5PkFYc+8yteleSsFZ/x/AP2vzPJY6vq66ageM6Kfa9N8q1V9YSqusX0un9VfUdV3bKqzqiq46ZpyZ/OjfviG2papGuVmk6vqodM3+15mU0R/ss5P9NXVNUPVdX+nz+6NrPA+eWp9jtV1U9Pz9rerqoeOB33yiTPrKpvrKo7Jvm5HPr7/P+T/LuqemDN3Hb6d3G7qvq2qnrwtOjV55N8bsX3AMDABF4A5vGHVXV9ZqNoP5vkl5OcvcqxJ2W2ENG+JP8ryYu7+43Tvl/ILMR8qqqeehj3f3mS38psCu2tk/xUMls1OslPJvmNzEYeP5PZgln7/ffp7z9W1cGeXX3pdO0/T/KhzMLQkw+jrpWePN3/g5mNfP/OdP01dffrk/znJG/IbDr4Gw445FeS/FNmIfWiJL+94tzrk5ya2TPEH83sO3pBvhr8n5Bkb1V9Osm/y2y6c7r7bzILlR+c+uNGU3y7+/1JfiSzBaY+meSRmS049k/zfKYD3D/JW6pqX5KLkzxlem76+swW6nrkVPcHMltALEmel+TtSd6V5IokfzW1HVR3vz3Jj2W26Ni1mX2PZ027b5XZz2l9crrPN2X2vDYAg6u11xEBAI60quokJ03PnwIAN4ERXgAAAIYk8AIAADAkU5oBAAAYkhFeAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhrRlswtYhDve8Y69ffv2zS5jCJ/5zGdy29vedrPLYA76ajnop+Wgn5aDfloe+mo56KfloJ9mLr/88k929zeuddyQgXf79u15+9vfvtllDGHPnj3ZuXPnZpfBHPTVctBPy0E/LQf9tDz01XLQT8tBP81U1YfnOc6UZgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJC2bHYBLI/tuy650fbeC07fpEoAAADWZoQXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIS008FbV8VX16qr6m6p6X1V9d1XdoaouraoPTH9vPx1bVfWiqrqyqt5VVfddcZ0zp+M/UFVnLrJmAAAAxrDoEd5fTfJH3f3tSe6d5H1JdiW5rLtPSnLZtJ0kD0ty0vQ6N8mvJUlV3SHJ+UkemOQBSc7fH5IBAABgNQsLvFV1XJJ/keQlSdLd/9Tdn0ry6CQXTYddlOQx0/tHJ3lZz7w5yfFVdackD01yaXdf093XJrk0yWmLqhsAAIAxVHcv5sJV90lyYZL3Zja6e3mSpyS5qruPn46pJNd29/FV9dokF3T3m6Z9lyV5WpKdSW7d3c+b2p+V5HPd/UsH3O/czEaGs23btvvt3r17IZ/rWLNv375s3bo1SXLFVdfdaN/JdzluM0piFSv7iqOXfloO+mk56Kfloa+Wg35aDvpp5pRTTrm8u3esddyWBdawJcl9kzy5u99SVb+ar05fTpJ0d1fVhiTu7r4ws4CdHTt29M6dOzfisse8PXv2ZP93edauS260b+8ZO498QaxqZV9x9NJPy0E/LQf9tDz01XLQT8tBPx2eRT7D+5EkH+nut0zbr84sAF89TVXO9PcT0/6rkpyw4vy7Tm2rtQMAAMCqFhZ4u/vjSf6+qr5tanpIZtObL06yf6XlM5O8Znp/cZInTqs1PyjJdd39sSR/nOTUqrr9tFjVqVMbAAAArGqRU5qT5MlJfruqbpnkg0nOzixkv6qqzkny4SSPm459XZKHJ7kyyWenY9Pd11TVc5O8bTruOd19zYLrBgAAYMktNPB29zuTHOxB4occ5NhO8qRVrvPSJC/d2OoAAAAY2aJ/hxcAAAA2hcALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCFt2ewCWF7bd11yo+29F5y+SZUAAAB8LSO8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADGmhgbeq9lbVFVX1zqp6+9R2h6q6tKo+MP29/dReVfWiqrqyqt5VVfddcZ0zp+M/UFVnLrJmAAAAxnAkRnhP6e77dPeOaXtXksu6+6Qkl03bSfKwJCdNr3OT/FoyC8hJzk/ywCQPSHL+/pAMAAAAq9mMKc2PTnLR9P6iJI9Z0f6ynnlzkuOr6k5JHprk0u6+pruvTXJpktOOdNEAAAAsl+ruxV286kNJrk3SSX69uy+sqk919/HT/kpybXcfX1WvTXJBd79p2ndZkqcl2Znk1t39vKn9WUk+192/dMC9zs1sZDjbtm273+7duxf2uY4l+/bty9atW5MkV1x13SGPPfkuxx2JkljFyr7i6KWfloN+Wg76aXnoq+Wgn5aDfpo55ZRTLl8xi3hVWxZcx/d191VV9U1JLq2qv1m5s7u7qjYkcXf3hUkuTJIdO3b0zp07N+Kyx7w9e/Zk/3d51q5LDnns3jN2Lr4gVrWyrzh66afloJ+Wg35aHvpqOein5aCfDs9CpzR391XT308k+f3MnsG9epqqnOnvJ6bDr0pyworT7zq1rdYOAAAAq1pY4K2q21bV7fa/T3JqkncnuTjJ/pWWz0zymun9xUmeOK3W/KAk13X3x5L8cZJTq+r202JVp05tAAAAsKpFTmneluT3Z4/pZkuS3+nuP6qqtyV5VVWdk+TDSR43Hf+6JA9PcmWSzyY5O0m6+5qqem6St03HPae7r1lg3QAAAAxgYYG3uz+Y5N4Haf/HJA85SHsnedIq13ppkpdudI0AAACMazN+lggAAAAWTuAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIa0ZbMLYBzbd11yo+29F5y+SZUAAAAY4QUAAGBQAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIWza7AI5O23ddkiQ57+Qbctb0HgAAYJkY4QUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMKSFB96qunlVvaOqXjttn1hVb6mqK6vqd6vqllP7rabtK6f921dc4+lT+/ur6qGLrhkAAIDldyRGeJ+S5H0rtl+Q5Fe6+x5Jrk1yztR+TpJrp/ZfmY5LVd0zyeOTfGeS05K8uKpufgTqBgAAYImtGXir6ilV9fU185Kq+quqOnWei1fVXZOcnuQ3pu1K8uAkr54OuSjJY6b3j562M+1/yHT8o5Ps7u4vdPeHklyZ5AHzfTwAAACOVdXdhz6g6q+7+97TVOIfT/KsJC/v7vuuefGqVyf5hSS3S/LUJGclefM0ipuqOiHJ67v7XlX17iSndfdHpn1/m+SBSZ49nfOKqf0l0zmvPuBe5yY5N0m2bdt2v927d8/3DXBQV1x1XZJk222Sqz93065x8l2O28CKWMu+ffuydevWzS6DNein5aCfloN+Wh76ajnop+Wgn2ZOOeWUy7t7x1rHbZnjWjX9fXhmQfc908jroU+qekSST3T35VW1c477rEt3X5jkwiTZsWNH79y58FsO7axdlyRJzjv5hrzwinn+mXytvWfs3MCKWMuePXvi3/3RTz8tB/20HPTT8tBXy0E/LQf9dHjmSTKXV9WfJDkxydOr6nZJvjzHed+b5FFV9fAkt07y9Ul+NcnxVbWlu29IctckV03HX5XkhCQfqaotSY5L8o8r2vdbeQ4AAAAc1DyLVp2TZFeS+3f3Z5PcMsnZa53U3U/v7rt29/bMFp16Q3efkeSNSf7VdNiZSV4zvb942s60/w09m299cZLHT6s4n5jkpCRvnefDAQAAcOxac4S3u79cVVcnuec08rpeT0uyu6qel+QdSV4ytb8kycur6sok12QWkjNNoX5VkvcmuSHJk7r7SxtQBwAAAANbM8BW1QuS/HBmgXN/0Owkfz7vTbp7T5I90/sP5iCrLHf355P80CrnPz/J8+e9HwAAAMwzYvuYJN/W3V9YdDEAAACwUeZ5hveDSW6x6EIAAABgI80zwvvZJO+sqsuSfGWUt7t/amFVAQAAwDrNE3gvnl4AAACwNOZZpfmiqrplkm+dmt7f3V9cbFkAAACwPvOs0rwzyUVJ9iapJCdU1ZndPfcqzQAAAHCkzTOl+YVJTu3u9ydJVX1rklcmud8iCwMAAID1mGeV5lvsD7tJ0t3/O1ZtBgAA4Cg3zwjv26vqN5K8Yto+I8nbF1cSAAAArN88gfcnkjwpyf6fIfqLJC9eWEUAAACwAeZZpfkLSX55egEAAMBSWDXwVtWruvtxVXVFkj5wf3d/10IrAwAAgHU41AjvU6a/jzgShQAAAMBGWnWV5u7+2PT2J7v7wytfSX7yyJQHAAAAN808P0v0gwdpe9hGFwIAAAAb6VDP8P5EZiO531JV71qx63ZJ/ueiCwMAAID1ONQzvL+T5PVJfiHJrhXt13f3NQutCgAAANbpUM/wXtfde5M8M8nHp2d3T0zyI1V1/BGqDwAAAG6SeZ7h/b0kX6qqeyS5MMkJmY3+AgAAwFFrnsD75e6+Icljk/yX7v4PSe602LIAAABgfeYJvF+sqn+d5IlJXju13WJxJQEAAMD6zRN4z07y3Ume390fqqoTk7x8sWUBAADA+hxqleYkSXe/t6qeluRu0/aHkrxg0YUBAADAeqw5wltVj0zyziR/NG3fp6ouXnRhAAAAsB7zTGl+dpIHJPlUknT3O5N88wJrAgAAgHWba9Gq7r7ugLYvL6IYAAAA2ChrPsOb5D1V9W+S3LyqTkryU0n+crFlAQAAwPrMM8L75CTfmeQLSV6Z5NNJfnqRRQEAAMB6zbNK82eT/Oz0AgAAgKWwZuCtqjcm6QPbu/vBC6kIAAAANsA8z/A+dcX7Wyf5l0luWEw5AAAAsDHmmdJ8+QFN/7Oq3rqgegAAAGBDzDOl+Q4rNm+W5H5JjltYRQAAALAB5pnSfHlmz/BWZlOZP5TknEUWBQAAAOs1z5TmE49EIQAAALCR5pnS/NhD7e/u/7Fx5QAAAMDGmGdK8zlJvifJG6btU5L8ZZJ/yGyqs8ALAADAUWeewHuLJPfs7o8lSVXdKclvdffZC60MAAAA1uFmcxxzwv6wO7k6yd0WVA8AAABsiHlGeC+rqj9O8spp+4eT/OniSgIAAID1m2eV5n9fVf93kn8xNV3Y3b+/2LIAAABgfeYZ4c0UcIVcAAAAlsY8z/ACAADA0hF4AQAAGNKqgbeqLpv+vuDIlQMAAAAb41DP8N6pqr4nyaOqaneSWrmzu/9qoZUBAADAOhwq8P5ckmcluWuSXz5gXyd58KKKAgAAgPVaNfB296uTvLqqntXdzz2CNQEAAMC6zfM7vM+tqkflq7/Du6e7X7vYsgAAAGB91lyluap+IclTkrx3ej2lqv7TogsDAACA9VhzhDfJ6Unu091fTpKquijJO5I8Y5GFAQAAwHrM+zu8x694f9wiCgEAAICNNM8I7y8keUdVvTGznyb6F0l2LbQqAAAAWKd5Fq16ZVXtSXL/qelp3f3xhVYFAAAA6zTPCG+6+2NJLl5wLQAAALBh5n2GFwAAAJaKwAsAAMCQDhl4q+rmVfU3R6oYAAAA2CiHDLzd/aUk76+qux2hegAAAGBDzLNo1e2TvKeq3prkM/sbu/tRC6sKAAAA1mmewPushVcBAAAAG2ye3+H9s6q6e5KTuvtPq+rrktx88aUBAADATbfmKs1V9WNJXp3k16emuyT5g0UWBQAAAOs1z88SPSnJ9yb5dJJ09weSfNMiiwIAAID1mifwfqG7/2n/RlVtSdKLKwkAAADWb57A+2dV9Ywkt6mqH0zy35P84WLLAgAAgPWZJ/DuSvIPSa5I8uNJXpfkmYssCgAAANZrnlWav1xVFyV5S2ZTmd/f3aY0AwAAcFRbM/BW1elJ/luSv01SSU6sqh/v7tcvujgAAAC4qdYMvElemOSU7r4ySarqW5JckkTgBQAA4Kg1zzO81+8Pu5MPJrl+QfUAAADAhlg18FbVY6vqsUneXlWvq6qzqurMzFZofttaF66qW1fVW6vqr6vqPVX181P7iVX1lqq6sqp+t6puObXfatq+ctq/fcW1nj61v7+qHrrOzwwAAMAx4FAjvI+cXrdOcnWS70+yM7MVm28zx7W/kOTB3X3vJPdJclpVPSjJC5L8SnffI8m1Sc6Zjj8nybVT+69Mx6Wq7pnk8Um+M8lpSV5cVTc/jM8IAADAMWjVZ3i7++z1XHhayXnftHmL6dVJHpzk30ztFyV5dpJfS/Lo6X2SvDrJf62qmtp3d/cXknyoqq5M8oAk/2s99QEAADC2WusXhqrqxCRPTrI9KwJydz9qzYvPRmIvT3KPJP9fkl9M8uZpFDdVdUKS13f3varq3UlO6+6PTPv+NskDMwvBb+7uV0ztL5nOefUB9zo3yblJsm3btvvt3r17rfI4hCuuui5Jsu02ydWfu2nXOPkux21gRaxl37592bp162aXwRr003LQT8tBPy0PfbUc9NNy0E8zp5xyyuXdvWOt4+ZZpfkPkrwks2d3v3w4RXT3l5Lcp6qOT/L7Sb79cM4/zHtdmOTCJNmxY0fv3LlzUbc6Jpy165IkyXkn35AXXjHPP5OvtfeMnRtYEWvZs2dP/Ls/+umn5aCfloN+Wh76ajnop+Wgnw7PPEnm8939ovXcpLs/VVVvTPLdSY6vqi3dfUOSuya5ajrsqiQnJPlIVW1JclySf1zRvt/KcwAAAOCg5vlZol+tqvOr6rur6r77X2udVFXfOI3spqpuk+QHk7wvyRuT/KvpsDOTvGZ6f/G0nWn/G6bngC9O8vhpFecTk5yU5K1zfj4AAACOUfOM8J6c5AmZLTa1f0rz/sWnDuVOSS6anuNKH7hRAAAWK0lEQVS9WZJXdfdrq+q9SXZX1fOSvCOz6dKZ/r58WpTqmsxWZk53v6eqXpXkvUluSPKkaao0R7nt07To/fZecPomVQIAAByL5gm8P5Tkm7v7nw7nwt39riT//CDtH8xsleUD2z8/3etg13p+kucfzv0BAAA4ts0zpfndSY5fdCEAAACwkeYZ4T0+yd9U1duSfGF/4zw/SwQAAACbZZ7Ae/7CqwAAAIANtmbg7e4/OxKFAAAAwEZaM/BW1fWZrcqcJLdMcoskn+nur19kYQAAALAe84zw3m7/+6qqJI9O8qBFFgUAAADrNc8qzV/RM3+Q5KELqgcAAAA2xDxTmh+7YvNmSXYk+fzCKgIAAIANMM8qzY9c8f6GJHszm9YMAAAAR615nuE9+0gUAgAAABtp1cBbVT93iPO6u5+7gHoAAABgQxxqhPczB2m7bZJzknxDEoEXAACAo9aqgbe7X7j/fVXdLslTkpydZHeSF652HgAAABwNDvkMb1XdIcnPJDkjyUVJ7tvd1x6JwgAAAGA9DvUM7y8meWySC5Oc3N37jlhVAAAAsE43O8S+85LcOckzk3y0qj49va6vqk8fmfIAAADgpjnUM7yHCsMAAABwVBNqAQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSFs2uwCOHdt3XXKj7b0XnL5JlQAAAMcCI7wAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIa0sMBbVSdU1Rur6r1V9Z6qesrUfoequrSqPjD9vf3UXlX1oqq6sqreVVX3XXGtM6fjP1BVZy6qZgAAAMaxyBHeG5Kc1933TPKgJE+qqnsm2ZXksu4+Kcll03aSPCzJSdPr3CS/lswCcpLzkzwwyQOSnL8/JAMAAMBqFhZ4u/tj3f1X0/vrk7wvyV2SPDrJRdNhFyV5zPT+0Ule1jNvTnJ8Vd0pyUOTXNrd13T3tUkuTXLaouoGAABgDNXdi79J1fYkf57kXkn+rruPn9orybXdfXxVvTbJBd39pmnfZUmelmRnklt39/Om9mcl+Vx3/9IB9zg3s5HhbNu27X67d+9e+Oca2RVXXZck2Xab5OrPLeYeJ9/luMVc+Bi1b9++bN26dbPLYA36aTnop+Wgn5aHvloO+mk56KeZU0455fLu3rHWcVsWXUhVbU3ye0l+urs/Pcu4M93dVbUhibu7L0xyYZLs2LGjd+7cuRGXPWadteuSJMl5J9+QF16xmH8me8/YuZDrHqv27NkT/+6PfvppOein5aCfloe+Wg76aTnop8Oz0FWaq+oWmYXd3+7u/zE1Xz1NVc709xNT+1VJTlhx+l2nttXaAQAAYFWLXKW5krwkyfu6+5dX7Lo4yf6Vls9M8poV7U+cVmt+UJLruvtjSf44yalVdftpsapTpzYAAABY1SKnNH9vkickuaKq3jm1PSPJBUleVVXnJPlwksdN+16X5OFJrkzy2SRnJ0l3X1NVz03ytum453T3NQusGwAAgAEsLPBOi0/VKrsfcpDjO8mTVrnWS5O8dOOqAwAAYHQLfYYXAAAANovACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJAEXgAAAIYk8AIAADAkgRcAAIAhCbwAAAAMSeAFAABgSAIvAAAAQxJ4AQAAGJLACwAAwJC2bHYBHLu277rkRtt7Lzh9kyoBAABGZIQXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwpC2bXQDst33XJTfa3nvB6ZtUCQAAMAIjvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAY0sICb1W9tKo+UVXvXtF2h6q6tKo+MP29/dReVfWiqrqyqt5VVfddcc6Z0/EfqKozF1UvAAAAY1nkCO9vJTntgLZdSS7r7pOSXDZtJ8nDkpw0vc5N8mvJLCAnOT/JA5M8IMn5+0MyAAAAHMrCAm93/3mSaw5ofnSSi6b3FyV5zIr2l/XMm5McX1V3SvLQJJd29zXdfW2SS/O1IRoAAAC+RnX34i5etT3Ja7v7XtP2p7r7+Ol9Jbm2u4+vqtcmuaC73zTtuyzJ05LsTHLr7n7e1P6sJJ/r7l86yL3OzWx0ONu2bbvf7t27F/a5jgVXXHVdkmTbbZKrP7c5NZx8l+M258ZLat++fdm6detml8Ea9NNy0E/LQT8tD321HPTTctBPM6eccsrl3b1jreO2HIliDqa7u6o2LG1394VJLkySHTt29M6dOzfq0seks3ZdkiQ57+Qb8sIrNuefyd4zdm7KfZfVnj174t/90U8/LQf9tBz00/LQV8tBPy0H/XR4jvQqzVdPU5Uz/f3E1H5VkhNWHHfXqW21dgAAADikIx14L06yf6XlM5O8ZkX7E6fVmh+U5Lru/liSP05yalXdflqs6tSpDQAAAA5pYXNVq+qVmT2De8eq+khmqy1fkORVVXVOkg8nedx0+OuSPDzJlUk+m+TsJOnua6rquUneNh33nO4+cCEsAAAA+BoLC7zd/a9X2fWQgxzbSZ60ynVemuSlG1gaAAAAx4AjPaUZAAAAjgiBFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDWtgqzbBe23ddcqPtvRecvkmVAAAAy8gILwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQtmx2ATCv7bsuudH23gtO36RKAACAZWCEFwAAgCEJvAAAAAxJ4AUAAGBIAi8AAABDEngBAAAYksALAADAkAReAAAAhiTwAgAAMCSBFwAAgCFt2ewC4KbavuuSG23vveD0TaoEAAA4GhnhBQAAYEgCLwAAAEMSeAEAABiSwAsAAMCQBF4AAACGJPACAAAwJIEXAACAIfkdXobhd3kBAICVjPACAAAwJIEXAACAIQm8AAAADEngBQAAYEgCLwAAAEOySjPHDKs4AwDAsUXgJcnXhkEAAIBlZ0ozAAAAQxJ4AQAAGJIpzRyzPNMLAABjM8ILAADAkAReAAAAhiTwAgAAMCSBFwAAgCFZtAomFrECAICxGOEFAABgSEZ4YRVGfAEAYLkZ4QUAAGBIRnhhHYwCAwDA0UvghTkdGG4BAICjm8ALG8iILwAAHD08wwsAAMCQjPAeo0zPPTKM+AIAwOYReOEIEoABAODIEXhhE6010i4QAwDATSfwwlHMiDAAANx0Ai8ssZWB+LyTb8jOzSsFAACOOksTeKvqtCS/muTmSX6juy/Y5JKWikWqxrBWPx5uP681YmyEGYD/0969B9s13mEc/z7EpQ2KMqqCuASDEmpC3UbdijGCqsYYt+qoDoaaqlunVa0ZdZuR6jA6UpchSEtljLq32rpLRCIhRMQ0mZRixqUubXj6x3pPuhN755zTJGftvc/zmcnstd6z9lq/c3551z6/td71noiITtYRBa+kFYFfA/sBc4GnJU20PaPeyCI6W38L5L4UwEt7cSVFdUREREQsKx1R8AKjgFm2ZwNIuhUYDQzagjd3bKNbDfT/7cUL7OVRsPc29Ly/k5f1duFhae/0t+Nkast6tEG7jV5o9jOvO6aIiIhuINt1x9ArSUcAB9j+blk/BtjZ9qkN25wEnFRWtwRmDnig3Wkd4M26g4g+Sa46Q/LUGZKnzpA8dY7kqjMkT50heapsbHvd3jbqlDu8vbJ9LXBt3XF0G0nP2N6p7jiid8lVZ0ieOkPy1BmSp86RXHWG5KkzJE/9s0LdAfTRPGDDhvVhpS0iIiIiIiKiqU4peJ8GRkjaRNLKwBhgYs0xRURERERERBvriCHNthdIOhW4j+rPEo2zPb3msAaLDBPvHMlVZ0ieOkPy1BmSp86RXHWG5KkzJE/90BGTVkVERERERET0V6cMaY6IiIiIiIjolxS8ERERERER0ZVS8EZLkg6QNFPSLEnn1B1PVCRtKOlPkmZImi7p9NJ+gaR5kqaUfwfVHetgJ2mOpGklH8+UtrUlPSDp5fK6Vt1xDnaStmzoN1MkvSvpjPSp+kkaJ+kNSc83tDXtQ6qMLZ9ZUyXtWF/kg0uLPF0q6cWSizslrVnah0v6sKFfXVNf5INLizy1PM9JOrf0p5mSvlFP1INTi1zd1pCnOZKmlPb0qV7kGd5oStKKwEvAfsBcqpmyj7I9o9bAAknrA+vbnixpdWAScChwJPC+7ctqDTAWkjQH2Mn2mw1tlwBv2764XEhay/bZdcUYiyrnvnnAzsAJpE/VStKewPvAjba3LW1N+1D5Rf004CCq/F1pe+e6Yh9MWuRpf+DhMvHoLwFKnoYDd/dsFwOnRZ4uoMl5TtLWwHhgFPBl4EFgC9ufDGjQg1SzXC329cuBd2xfmD7Vu9zhjVZGAbNsz7b9b+BWYHTNMQVge77tyWX5PeAFYIN6o4p+GA3cUJZvoLpYEe1jH+AV26/VHUiA7b8Aby/W3KoPjab65dC2nwDWLBcIYzlrlifb99teUFafAIYNeGCxiBb9qZXRwK22P7b9KjCL6nfDGABLypUkUd3kGD+gQXWwFLzRygbA3xvW55Kiqu2Uq3o7AE+WplPL8LFxGSrbFgzcL2mSpJNK23q255flfwDr1RNatDCGRX+JSJ9qP636UD632td3gD82rG8i6VlJj0jao66gYqFm57n0p/a1B/C67Zcb2tKnliAFb0SHkrQa8HvgDNvvAlcDmwEjgfnA5TWGF5Xdbe8IHAicUoYoLeTqmZI8V9ImJK0MHAJMKE3pU20ufaj9STofWADcXJrmAxvZ3gE4E7hF0hp1xRc5z3Wgo1j0wmz6VC9S8EYr84ANG9aHlbZoA5JWoip2b7Z9B4Dt121/YvtT4Ddk6FHtbM8rr28Ad1Ll5PWeYZbl9Y36IozFHAhMtv06pE+1sVZ9KJ9bbUbS8cDBwNHl4gRliOxbZXkS8AqwRW1BDnJLOM+lP7UhSUOAw4HbetrSp3qXgjdaeRoYIWmTctdjDDCx5piChc9uXAe8YPuKhvbGZ9UOA55f/L0xcCQNLZOKIWkosD9VTiYCx5XNjgPuqifCaGKRq+bpU22rVR+aCBxbZmvehWpCl/nNdhDLn6QDgB8Bh9j+oKF93TI5HJI2BUYAs+uJMpZwnpsIjJG0iqRNqPL01EDHF5+xL/Ci7bk9DelTvRtSdwDRnsqsiqcC9wErAuNsT685rKjsBhwDTOuZkh44DzhK0kiq4X1zgO/VE14U6wF3VtcnGALcYvteSU8Dt0s6EXiNauKJqFm5KLEfi/abS9Kn6iVpPLAXsI6kucBPgYtp3ofuoZqheRbwAdUs2zEAWuTpXGAV4IFyHnzC9snAnsCFkv4DfAqcbLuvEynFUmiRp72anedsT5d0OzCDakj6KZmheeA0y5Xt6/jsPBOQPtWr/FmiiIiIiIiI6EoZ0hwRERERERFdKQVvREREREREdKUUvBEREREREdGVUvBGREREREREV0rBGxEREREREV0pBW9EREQfSRom6S5JL0uaLekqSass42McKmnrhvULJe27LI8RERExWKTgjYiI6ANVf0z0DuAPtkcAI4DPAZcs40MdCiwseG3/xPaDy/gYS03SkLpjiIiI6E0K3oiIiL7ZG/jI9m8BbH8C/AA4VtJqko6XdFXPxpLulrRXWd5f0uOSJkuaIGm10n6xpBmSpkq6TNKuwCHApZKmSNpM0vWSjijb7yPpWUnTJI3rubssaY6kn5X9T5O01eLBS9pG0lNlv1MljSjtx5b15yTdVNqGS3q4tD8kaaPSfr2kayQ9CVwiaWiJ46kS1+glHSsiImKgpeCNiIjom22ASY0Ntt8F5gCbt3qTpHWAHwP72t4ReAY4U9IXgcOAbWxvB/zC9mPAROAs2yNtv9Kwn1WB64Fv2/4KMAT4fsOh3iz7vxr4YZNQTgautD0S2AmYK2mbEtvetrcHTi/b/gq4ocR1MzC2YT/DgF1tnwmcDzxsexTwdapCfWizY7X6+URERCxPKXgjIiKWr12ohig/KmkKcBywMfAO8BFwnaTDgQ962c+WwKu2XyrrNwB7Nnz9jvI6CRje5P2PA+dJOhvY2PaHVHetJ9h+E8D222XbrwG3lOWbgN0b9jOh3N0G2B84p3xffwZWBTZqcayIiIgBl4I3IiKib2YAX21skLQG8CVgJrCART9XV+3ZDHig3LEdaXtr2yfaXgCMAn4HHAzcu5TxfVxeP6G6+7sI27dQDZf+ELhH0t7/53H+1bAs4JsN39tGtl9YhseKiIhYKil4IyIi+uYh4POSjgWQtCJwOXBVuYM5BxgpaQVJG1IVswBPALtJ2ry8b6ikLcpzvF+wfQ/Vs8Dbl+3fA1ZvcvyZwPCe/QDHAI/0NXhJmwKzbY8F7gK2Ax4GvlWGVyNp7bL5Y8CYsnw08NcWu70POK1M6IWkHZZwrIiIiAGXgjciIqIPbJvqmdsjJL0MvAV8avuissmjwKtUd4LHApPL+/4JHA+MlzSVarjvVlRF7d2l7W/AmWU/twJnlUmgNms4/kfACcAESdOAT4Fr+vEtHAk8X4YfbwvcaHs6cBHwiKTngCvKtqcBJ5TYjuF/z/Yu7ufASsBUSdPLetNj9SPOiIiIZUbV53dERET0R5lReTxwmO3JdccTERERn5WCNyIiIiIiIrpShjRHREREREREV0rBGxEREREREV0pBW9ERERERER0pRS8ERERERER0ZVS8EZERERERERXSsEbERERERERXem/R5ws095UOeoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,10))\n",
    "fig.suptitle(\"Distribution of question scores\")\n",
    "plt.xlabel(\"Question scores\")\n",
    "plt.ylabel(\"Number of questions\")\n",
    "df[\"Score\"].hist(bins=200);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's dive more into the questions by looking at potential predictive features, such as question length. How long are the questions in our dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,10))\n",
    "fig.suptitle(\"Distribution of question length for sentences\")\n",
    "plt.xlabel(\"Words per question\")\n",
    "plt.ylabel(\"Number of questions\")\n",
    "q_len = plt.hist(df[\"text_len\"],  bins=2000,log=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only a few outliers seem to contain more than two thousand words. Let's zoom in to the shorter questions on the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,10))\n",
    "fig.suptitle(\n",
    "    \"Distribution of question length for sentences shorter than 2000 words\")\n",
    "plt.xlabel(\"Words per question\")\n",
    "plt.ylabel(\"Number of questions\")\n",
    "q_len_trunc = plt.hist(df[df[\"text_len\"]<2000][\"text_len\"], bins=200, log=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now is sentence length predictive of getting many upvotes? \n",
    "\n",
    "In order to start answering this question, we can update this plot to overlay the distributions for both high and low score questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "high_score = df[\"Score\"] > df[\"Score\"].median()\n",
    "# We filter out really long questions\n",
    "normal_length = df[\"text_len\"] < 2000\n",
    "\n",
    "ax = df[df[\"is_question\"] & high_score & normal_length][\"text_len\"].hist(\n",
    "    bins=60,\n",
    "    density=True,\n",
    "    histtype=\"step\",\n",
    "    color=\"orange\",\n",
    "    linewidth=3,\n",
    "    grid=False,\n",
    "    figsize=(16, 10),\n",
    ")\n",
    "\n",
    "df[df[\"is_question\"] & ~high_score & normal_length][\"text_len\"].hist(\n",
    "    bins=60,\n",
    "    density=True,\n",
    "    histtype=\"step\",\n",
    "    color=\"purple\",\n",
    "    linewidth=3,\n",
    "    grid=False,\n",
    ")\n",
    "\n",
    "handles = [\n",
    "    Rectangle((0, 0), 1, 1, color=c, ec=\"k\") for c in [\"orange\", \"purple\"]\n",
    "]\n",
    "labels = [\"High score\", \"Low score\"]\n",
    "plt.legend(handles, labels)\n",
    "ax.set_xlabel(\"Sentence length (characters)\")\n",
    "ax.set_ylabel(\"Percentage of sentences\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that there may be a relatively higher proportion of very short questions amongst unanswered ones. Questions with high scores tend to be longer on average. This makes question length a feature candidate that we should try for our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Going back to using \"received answers\" as a measure of quality, let's look at the number of answers questions get, and more precisely the distribution of the quantity of answers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,10))\n",
    "fig.suptitle(\"Distribution of number of answers\")\n",
    "plt.xlabel(\"Number of answers\")\n",
    "plt.ylabel(\"Number of questions\")\n",
    "hist = df[df[\"is_question\"]][\"AnswerCount\"].hist(bins=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if questions with high scores tend to get more answers. \n",
    "\n",
    "First, let's plot one as a function of the other, and see whether we can establish a trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter = df[df[\"is_question\"]][[\"Score\", \"AnswerCount\"]].plot(x=\"Score\", y=\"AnswerCount\", \n",
    "                                                               kind=\"scatter\",\n",
    "                                                              figsize=(16, 10))\n",
    "ax.set_xlabel(\"Score\")\n",
    "ax.set_ylabel(\"Num answers\")\n",
    "scatter.set_title(\"Answer counts as a function of question score\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scores and number of answers seem correlated. We mentioned above making the score into a binary label by using `is_score_above_median` as the label. Let's see how this label would correlate with the number of answers by plotting histograms of the numbers of answers a question gets for question in each category (score above and below the median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,10))\n",
    "fig.suptitle(\"Distribution of number of answers for high and low score questions\")\n",
    "plt.xlim(-5,80)\n",
    "\n",
    "ax = df[df[\"is_question\"] &\n",
    "        (df[\"Score\"] > df[\"Score\"].median())][\"AnswerCount\"].hist(bins=60,\n",
    "                                                          density=True,\n",
    "                                                          histtype=\"step\",\n",
    "                                                          color=\"orange\",\n",
    "                                                          linewidth=3,\n",
    "                                                          grid=False,\n",
    "                                                          figsize=(16, 10))\n",
    "\n",
    "df[df[\"is_question\"] &\n",
    "   ~(df[\"Score\"] > df[\"Score\"].median())][\"AnswerCount\"].hist(bins=60,\n",
    "                                                     density=True,\n",
    "                                                     histtype=\"step\",\n",
    "                                                     color=\"purple\",\n",
    "                                                     linewidth=3,\n",
    "                                                     grid=False)\n",
    "\n",
    "handles = [Rectangle((0, 0), 1, 1, color=c, ec=\"k\") for c in\n",
    "           [\"orange\", \"purple\"]]\n",
    "labels = [\"High score\", \"Low score\"]\n",
    "plt.legend(handles, labels)\n",
    "ax.set_xlabel(\"Num answers\")\n",
    "ax.set_ylabel(\"Percentage of sentences\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like questions that get a high score get more answers. The score of a question thus seems like a good initial label since it is more granular, but the number of answers would be a fine choice as well. \n",
    "\n",
    "While it would make a good label, we cannot use the number of answers as a feature. This is because despite being correlated with the score, as the plot above shows, we will not have access to it at inference time, when we receive input from the users.\n",
    "\n",
    "Since our model needs to work without having access to this input, we can either ignore it completely during training, or use it as a feature during training, and set the value of this feature as a constant in production. The first approach is simpler, but the second approach can help a model separate the effect of the \"number of answers\" feature from other style effects which we do want to capture. I'll let the reader experiment with both approaches.\n",
    "\n",
    "After this summary exploration, we will see how to [split data](https://github.com/hundredblocks/ml-powered-applications/blob/master/notebooks/splitting_data.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml_editor",
   "language": "python",
   "name": "ml_editor"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
